Combined efforts in the fields of neuroscience, computer science, and biology allowed to design biologically realistic models of the brain based on spiking neural networks. For a proper validation of these models, an embodiment in a dynamic and rich sensory environment, where the model is exposed to a realistic sensory-motor task, is needed. Due to the complexity of these brain models that, at the current stage, cannot deal with real-time constraints, it is not possible to embed them into a real-world task. Rather, the embodiment has to be simulated as well. While adequate tools exist to simulate either complex neural networks or robots and their environments, there is so far no tool that allows to easily establish a communication between brain and body models. The Neurorobotics Platform is a new web-based environment that aims to fill this gap by offering scientists and technology developers a software infrastructure allowing them to connect brain models to detailed simulations of robot bodies and environments and to use the resulting neurorobotic systems for in silico experimentation. In order to simplify the workflow and reduce the level of the required programming skills, the platform provides editors for the specification of experimental sequences and conditions, environments, robots, and brain–body connectors. In addition to that, a variety of existing robots and environments are provided. This work presents the architecture of the first release of the Neurorobotics Platform developed in subproject 10 “Neurorobotics” of the Human Brain Project (HBP).1 At the current state, the Neurorobotics Platform allows researchers to design and run basic experiments in neurorobotics using simulated robots and simulated environments linked to simplified versions of brain models. We illustrate the capabilities of the platform with three example experiments: a Braitenberg task implemented on a mobile robot, a sensory-motor learning task based on a robotic controller, and a visual tracking embedding a retina model on the iCub humanoid robot. These use-cases allow to assess the applicability of the Neurorobotics Platform for robotic tasks as well as in neuroscientific experiments.

Bio-inspired robots still rely on classic robot control although advances in neurophysiology allow adaptation to control as well. However, the connection of a robot to spiking neuronal networks needs adjustments for each purpose and requires frequent adaptation during an iterative development. Existing approaches cannot bridge the gap between robotics and neuroscience or do not account for frequent adaptations. The contribution of this paper is an architecture and domain-specific language (DSL) for connecting robots to spiking neuronal networks for iterative testing in simulations, allowing neuroscientists to abstract from implementation details. The framework is implemented in a web-based platform. We validate the applicability of our approach with a case study based on image processing for controlling a four-wheeled robot in an experiment setting inspired by Braitenberg vehicles.

Although robotics has made progress with respect to adaptability and interaction in natural environments, it cannot match the capabilities of biological systems. A promising approach to solve this problem is to create biologically plausible robot controllers that use detailed neuronal networks. However, this approach yields a large gap between the neuronal network and its connection to the robot on the one side and the technical implementation on the other. Existing approaches neglect bridging this gap between disciplines and their focus on different abstractions layers but manually hand-craft the simulations. This makes the tight technical integration cumbersome and error-prone impairing round-trip validation and academic advancements. Our approach maps the problem to model-driven engineering techniques and defines a domain-specific language (DSL) for integrating biologically plausible Neuronal Networks in robot control algorithms. It provides different levels of abstraction and sets an interface standard for integration. Our approach is implemented in the Neuro-Robotics Platform (NRP) of the Human Brain Project (HBP 1). Its practical applicability is validated in a minimalist experiment inspired by the Braitenberg vehicles based on the simulation of a four-wheeled Husky 2 robot controlled by a neuronal network.

Developing neuro-inspired computing paradigms that mimic nervous system function is an emerging field of research that fosters our model understanding of the biological system and targets technical applications in artificial systems. The computational power of simulated brain circuits makes them a very promising tool for the development for brain-controlled robots. Early phases of robotic controllers development make extensive use of simulators as they are easy, fast and cheap tools. In order to develop robotics controllers that encompass brain models, a tool that include both neural simulation and physics simulation is missing. Such a tool would require the capability of orchestrating and synchronizing both simulations as well as managing the exchange of data between them. The Neurorobotics Platform (NRP) aims at filling this gap through an integrated software toolkit enabling an experimenter to design and execute a virtual experiment with a simulated robot using customized brain models. As a use case for the NRP, the iCub robot has been integrated into the platform and connected to a spiking neural network. In particular, experiments of visual tracking have been conducted in order to demonstrate the potentiality of such a platform